Deep Learning: Potential and Opportunities for Insurers

The potential impact of Artificial Intelligence (AI) on areas including customer experience, underwriting, distribution, and claims has generated a lot of excitement in the insurance industry.Insurance carriers can greatly benefit from recent advances in artificial intelligence and machine learning. These techniques are potential game changers, especially in the area of fraud analytics.

Deep learning describes a type of machine learning algorithm that uses multi-layered neural networks. With these algorithms, machines can learn new skills as they operate, such as how to recognize images and understand specific patterns in speech and unstructured text, among other applications.

Specifically, models known as Convolutional Neural Networks (CNNs) were developed and applied to computer vision applications in autonomous vehicles and robots. In recent years, CNN technology has been successfully adapted to text processing. Although proprietary versions of the software are available from vendors (e.g., IBM), open source versions of the software are also readily available to data scientists.What makes CNNs unique is that they learn to spot features on their own through training. CNNs were made possible by the tremendous progress in Graphical Processing Units and parallel processing in the past decade. The internet has also made a profound difference by providing virtually unlimited training sets of digital images, videos, and text to feed CNNs’ insatiable appetite for data.

Deep Learning Has Already Started to Disrupt the Insurance Industry

What makes CNNs unique is that they learn to spot features on their own through training

With huge volumes of structured and unstructured data coming in from a variety of sources including web-scraping, third-party databases and the Internet of Things, insurers can use deep learning not only to assess signals contained in unstructured text and images but also to recognize patterns and draw conclusions related to risk.

The state of science and technology, meanwhile, is continuing to evolve and become more powerful in automating insurance processes. The insurance industry is beginning to see deep learning as especially useful in managing claims because it can help insurers not only assess claims at high speed but identify anomalies indicating potential claims fraud. A machine that can recognize patterns in fraud could help companies ferret out false claims and determine payouts for legitimate claims faster and more accurately.

Potential applications for deep learning being explored by insurers include image recognition for auto claims (and other functions for the driverless car), customer service functions for maximizing cross-sell and up-sell opportunities with real time personalized offers, and actuarial functions for improving product pricing and modeling catastrophe risk.

Early stage results using CNNs and Deep Learning to create anti-fraud models are especially promising. Comprehensive testing has demonstrated that these CNN models provide a significant improvement in predictive power when compared to existing models currently in use. Another unexpected and extremely powerful feature of CNNs is their generality and robustness.

One of the main challenges in using CNNs (as well as machine learning algorithms in general) is the relative lack of transparency in providing detailed explanations for the prediction. This can be a serious limitation in rolling out these models, as experts need to understand why some claims were singled out for a fraud alert and not others in order to conduct the necessary follow-ups. In our experience, the development of reason code engines is still in its infancy and we encourage additional research to ensure that these algorithms are ready for prime time in the industry.

Considering technology’s progress to date in computer vision, future applications could include severity estimation for auto and property claims based on accident photos (auto-adjudication). CNNs have now evolved into Recurrent Neural Nets (RNNs) that might be better adapted to text mining because they can identify sequential relationships in unstructured data. For example, the time and order of the text and information captured may have predictive properties. These algorithms have the potential to improve the accuracy of predictions.